Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy.

PURPOSE: To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS: We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS: In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION: Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

[PET/CT in radiotherapy : indications and potential applications]. / TEP/TDM en radiothérapie : indications et perspectives.

Functional imaging, including positron emission tomography combined with computed tomography (PET/CT), allows the evaluation of numerous biological properties that could be considered at all steps of the therapeutic management of patients treated with radiotherapy. Indeed, it enables better initial staging of the disease, and some parameters may also be used as predictive biomarkers for treatment response, allowing better selection of patients eligible for radiotherapy. It may also improve the definition of target volumes with the aim of dose escalations by dose-painting. Finally, it could be useful during the follow-up to assess response to treatment. In this review, we report how functional imaging is integrated at the present time during the radiotherapy procedure, and what are its potential future contributions.

L'imagerie fonctionnelle, dont la tomographie par émission de positons couplée à la tomodensitométrie (TEP/TDM), permet l'évaluation de nombreuses propriétés biologiques qui pourraient être prises en compte à toutes les étapes de la prise en charge des patients traités par radiothérapie. En effet, elle permet une meilleure stadification initiale de la maladie, et certains paramètres peuvent également être utilisés comme biomarqueurs prédictifs de la réponse au traitement, permettant ainsi une meilleure sélection des patients éligibles à la radiothérapie. Elle peut également améliorer la définition des volumes cibles dans le but d'escalader la dose par dose-painting. Enfin, elle pourrait être utile lors du suivi pour évaluer la réponse au traitement. Dans cette revue, nous rapportons comment l'imagerie fonctionnelle est intégrée, à l'heure actuelle, au cours d'un traitement par radiothérapie, et nous discutons quelles sont ses futures contributions potentielles dans les principales localisations tumorales où la radiothérapie est recommandée.

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer.

PURPOSE: To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters. METHODS: We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. RESULTS: In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. CONCLUSIONS: Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

PD-L1 expression in recurrent head and neck squamous cell carcinoma.

PURPOSE: To evaluate the Programmed Cell Death Ligand (PD-L1) expression at diagnosis and relapse in patients with head and neck carcinoma (HNSCC) treated with radio(chemo)therapy. METHODS: PD-L1 immunohistochemistry was performed in tumor cells (TC) and immune cells (IC) in 44 patients and scored as 0 = 0%, 1 = < 5%, 2 = 6-49% or 3 = ≥ 50% cells. RESULTS: PD-L1 expression on TC before RT was scored as 0, 1, 2 and 3 in 28, 4, 8 and 4 patients, respectively. In 10 patients, IC did not show any PD-L1 expression; while in 8, 16, and 10 patients, PD-L1 expression was scored 1, 2 and 3, respectively. At relapse, 7/36 patients had a PD-L1 expression positivation in TC, while the opposite was observed in 6 patients. Overall, survival at 2 years was higher in patients with PD-L1 expression (90% versus 62.5%, p = 0.032). CONCLUSION: PD-L1 expression may vary throughout the course of the disease. A re-evaluation of PD-L1 expression on biopsies at the time of recurrence should be recommended.

[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.

PURPOSE: To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). METHODS: One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners. RESULTS: After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67-0.88), 0.49 (0.25-0.67), 0.42 (0.25-0.60) and 0.63 (0.20-0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set. CONCLUSION: [18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient's outcome but remain subject to variability across PET/CT devices.

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.

PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. METHODS: In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). RESULTS: The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. CONCLUSION: The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.

Impact of concomitant systemic treatments on toxicity and intracerebral response after stereotactic radiotherapy for brain metastases.

BACKGROUND: The aim of this study was to determine the safety and efficacy of fractionated stereotactic radiotherapy (SRT) in combination with systemic therapies (ST) for brain metastases (BM). METHODS: Ninety-nine patients (171 BM) received SRT and concurrent ST (group 1) and 95 patients (131 BM) received SRT alone without concurrent ST (group 2). SRT was planned on a linear accelerator, using volumetric modulated arc therapy. All ST were allowed including chemotherapy (CT), immunotherapy (IT), targeted therapy (TT) and hormonotherapy (HT). Treatment was considered to be concurrent if the timing between the drug administration and SRT did not exceed 1 month. Local control (LC), freedom for distant brain metastases (FFDBM), overall survival (OS) and radionecrosis (RN) were evaluated. RESULTS: After a median follow-up of 11.9 months (range 0.7-29.7), there was no significant difference between the two groups. However, patients who received concurrent IT (n = 30) had better 1-year LC, OS, FFDBM but a higher RN rate compared to patients who did not: 96% versus 78% (p = 0.02), 89% versus 77% (p = 0.02), 76% versus 53% (p = 0.004) and 80% versus 90% (p = 0.03), respectively. In multivariate analysis, concurrent IT (p = 0.022) and tumor volume < 2.07 cc (p = 0.039) were significantly correlated with improvement of LC. The addition of IT to SRT compared to SRT alone was associated with an increased risk of RN (p = 0.03). CONCLUSION: SRT delivered concurrently with IT seems to be associated with improved LC, FFDBM and OS as well as with a higher rate of RN.

External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.

PURPOSE: The aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC). METHODS: Patients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts. RESULTS: The DFS model reached an accuracy of 90% (95% CI [79-98%]) (sensitivity 92-93%, specificity 87-89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90-99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80-99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82-85% and 71-86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56-60% only. CONCLUSIONS: The previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

PURPOSE: The aim of this study is to determine if radiomics features from 18fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. METHODS: One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. RESULTS: In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non UniformityGLRLM in PET and EntropyGLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). CONCLUSIONS: In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

PET/CT-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non-Small Cell Lung Cancer Treated with Curative Radiotherapy.

In lung cancer patients, radiotherapy is associated with a increased risk of local relapse (LR) when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status (MutKEAP1/NFE2L2) is significantly correlated with LR in patients treated with radiotherapy but is rarely available. Prediction of MutKEAP1/NFE2L2 with noninvasive modalities could help to further personalize each therapeutic strategy. Methods: Based on a public cohort of 770 patients, model RNA (M-RNA) was first developed using continuous gene expression levels to predict MutKEAP1/NFE2L2, resulting in a binary output. The model PET/CT (M-PET/CT) was then built to predict M-RNA binary output using PET/CT-extracted radiomics features. M-PET/CT was validated on an external cohort of 151 patients treated with curative volumetric modulated arc radiotherapy. Each model was built, internally validated, and evaluated on a separate cohort using a multilayer perceptron network approach. Results: The M-RNA resulted in a C statistic of 0.82 in the testing cohort. With a training cohort of 101 patients, the retained M-PET/CT resulted in an area under the curve of 0.90 (P < 0.001). With a probability threshold of 20% applied to the testing cohort, M-PET/CT achieved a C statistic of 0.7. The same radiomics model was validated on the volumetric modulated arc radiotherapy cohort as patients were significantly stratified on the basis of their risk of LR with a hazard ratio of 2.61 (P = 0.02). Conclusion: Our approach enables the prediction of MutKEAP1/NFE2L2 using PET/CT-extracted radiomics features and efficiently classifies patients at risk of LR in an external cohort treated with radiotherapy.

The dosimetric parameters impact on local recurrence in stereotactic radiotherapy for brain metastases.

OBJECTIVES: Stereotactic radiotherapy (SRT) for brain metastases (BM) allows very good local control (LC). However, approximately 20%-30% of these lesions will recur. The objective of this retrospective study was to evaluate the impact of dosimetric parameters on LC in cerebral SRT. METHODS: Patients treated with SRT for 1-3 BM between January 2015 and December 2018 were retrospectively included. A total of 349 patients with 538 lesions were included. The median gross tumour volume (GTV) was 2 cm3 (IQR, 0-7). The median biological effective dose with α/ß = 10 (BED10) was 60 Gy (IQR, 32-82). The median prescription isodose was 71% (IQR, 70-80). Correlations with LC were examined using the Cox regression model. RESULTS: The median follow-up period was 55 months (min-max, 7-85). Median overall survival was 17.8 months (IQR, 15.2-21.9). There were 95 recurrences and LC at 1 and 2 years was 87.1% (95% CI, 84-90) and 78.1% (95% CI, 73.9-82.4), respectively. Univariate analysis showed that systemic treatment, dose to 2% and 50% of the planning target volume (PTV), BED10 > 50 Gy, and low PTV and GTV volume were significantly correlated with better LC. In the multivariate analysis, GTV volume, isodose, and BED10 were significantly associated with LC. CONCLUSION: These results show the importance of a BED10 > 50 Gy associated with a prescription isodose <80% to optimize LC during SRT for BM. ADVANCES IN KNOWLEDGE: Isodose, BED, and GTV volume were significantly associated with LC. A low isodose improves LC without increasing the risk of radionecrosis.

Correlation between rCBV Delineation Similarity and Overall Survival in a Prospective Cohort of High-Grade Gliomas Patients: The Hidden Value of Multimodal MRI?

PURPOSE: The accuracy of target delineation in radiation treatment planning of high-grade gliomas (HGGs) is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Magnetic resonance imaging (MRI) represents the standard imaging modality for delineation of gliomas with inherent limitations in accurately determining the microscopic extent of tumors. The purpose of this study was to assess the survival impact of multi-observer delineation variability of multiparametric MRI (mpMRI) and [18F]-FET PET/CT. MATERIALS AND METHODS: Thirty prospectively included patients with histologically confirmed HGGs underwent a PET/CT and mpMRI including diffusion-weighted imaging (DWI: b0, b1000, ADC), contrast-enhanced T1-weighted imaging (T1-Gado), T2-weighted fluid-attenuated inversion recovery (T2Flair), and perfusion-weighted imaging with computation of relative cerebral blood volume (rCBV) and K2 maps. Nine radiation oncologists delineated the PET/CT and MRI sequences. Spatial similarity (Dice similarity coefficient: DSC) was calculated between the readers for each sequence. Impact of the DSC on progression-free survival (PFS) and overall survival (OS) was assessed using Kaplan-Meier curves and the log-rank test. RESULTS: The highest DSC mean values were reached for morphological sequences, ranging from 0.71 +/- 0.18 to 0.84 +/- 0.09 for T2Flair and T1Gado, respectively, while metabolic volumes defined by PET/CT achieved a mean DSC of 0.75 +/- 0.11. rCBV variability (mean DSC0.32 +/- 0.20) significantly impacted PFS (p = 0.02) and OS (p = 0.002). CONCLUSIONS: Our data suggest that the T1-Gado and T2Flair sequences were the most reproducible sequences, followed by PET/CT. Reproducibility for functional sequences was low, but rCBV inter-reader similarity significantly impacted PFS and OS.

Uncommon MET mutational landscape in a non-small cell lung cancer patient treated with crizotinib: Case report.

Background: MET exon 14 (METex14) skipping mutations are oncogenic drivers observed in approximately 3-4% of non-small cell lung cancers (NSCLC). Several distinct genetic alterations leading to METex14 have been reported but clinical significances of rare mutations are not well defined as well as outcomes of patients upon MET inhibitors (METi). Case presentation: This report presents the case of a patient with metastatic NSCLC harboring an uncommon MET mutational landscape including notably a novel METex14 mutation (R1022L). Dramatic but transient efficacy was observed under crizotinib, due to early occurrence of acquired both on- and off-target mechanisms of resistance such as MET D1246H mutation and wild-type KRAS amplification. Conclusion: Our case provides additional data on MET rare oncogenic variants and their sensitivity to METi. Systematic assessment of post-tyrosine kinase inhibitor tumor sample remains critical to identify on- and off-target mechanisms that may represent therapeutically targetable drivers in resistant patients.

Predictive clinical and dosimetric parameters for risk of relapse in early-stage non-small cell lung cancer treated by SBRT: A large single institution experience.

Purpose: To evaluate the impact of dosimetric parameters on efficacy of stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (ES-NSCLC), using Hypofractionated Treatment Effects in the Clinic (HyTEC) reporting standards. Methods: From April 2010 to December 2020, 497 patients who received SBRT for ES-NSCLC at the University Hospital of Liège were retrospectively enrolled. A total dose of 40 to 60 Gy in 3-5 fractions (72-180 Gy biologically effective dose with an α/ß ratio of 10 (BED10)) was prescribed to the 80 % isodose line of the PTV. Potential clinical and dosimetric predictors of recurrence, overall survival (OS) and disease specific survival (DSS) were evaluated using univariate and multivariate analyses. Results: After a median follow-up of 32 months (range 3-143 months), the local control and disease-free survival (DFS) rates at 3 years were 91 % (95 % CI: 90 %-93 %) and 75 % (95 % CI: 73 %-77 %), respectively. The median OS was 41.6 months and the median DSS was not reached. On multivariate analysis, a higher gross tumor volume (GTV) Dmax (BED10) (cut-off 198 Gy) and a larger percent of the GTV receiving ≥110 % of the prescribed dose were predictive of a better local control, only GTV volume was correlated with DSS and no parameter was correlated with OS and regional or distant recurrences. Conclusion: Lung SBRT for ES-NSCLC in 3 to 5 fractions resulted in high local control rates. A higher percent of GTV receiving ≥110 % of the prescribed dose and a higher GTV Dmax (BED10) seem to allow a better local control.

Prediction of Acute Radiation-Induced Lung Toxicity After Stereotactic Body Radiation Therapy Using Dose-Volume Parameters From Functional Mapping on Gallium 68 Perfusion Positron Emission Tomography/Computed Tomography.

PURPOSE: The aim of this work was to compare anatomic and functional dose-volume parameters as predictors of acute radiation-induced lung toxicity (RILT) in patients with lung tumors treated with stereotactic body radiation therapy. METHODS AND MATERIALS: Fifty-nine patients treated with stereotactic body radiation therapy were prospectively included. All patients underwent gallium 68 lung perfusion positron emission tomography (PET)/computed tomography (CT) imaging before treatment. Mean lung dose (MLD) and volumes receiving x Gy (VxGy, 5-30 Gy) were calculated in 5 lung volumes: the conventional anatomic volume (AV) delineated on CT images, 3 lung functional volumes (FVs) defined on lung perfusion PET imaging (FV50%, FV70%, and FV90%; ie, the minimal volume containing 50%, 70%, and 90% of the total activity within the AV), and a low FV (LFV; LFV = AV - FV90%). The primary endpoint of this analysis was grade ≥2 acute RILT at 3 months as assessed with National Cancer Institute Common Terminology Criteria for Adverse Events version 5. Dose-volume parameters in patients with and without acute RILT were compared. Receiver operating characteristic curves assessing the ability of dose-volume parameters to discriminate between patients with and without acute RILT were generated, and area under the curve (AUC) values were calculated. RESULTS: Of the 59 patients, 10 (17%) had grade ≥2 acute RILT. The MLD and the VxGy in the AV and LFV were not statistically different between patients with and without acute RILT (P > .05). All functional parameters were significantly higher in acute RILT patients (P < .05). AUC values (95% CI) for MLD AV, LFV, FV50%, FV70%, and FV90% were 0.66 (0.46-0.85), 0.60 (0.39-0.80), 0.77 (0.63-0.91), 0.77 (0.64-0.91), and 0.75 (0.58-0.91), respectively. AUC values for V20Gy AV, LFV, FV50%, FV70%, and FV90% were 0.65 (0.44-0.87), 0.64 (0.46-0.83), 0.82 (0.69-0.95), 0.81 (0.67-0.96), and 0.75 (0.57-0.94), respectively. CONCLUSIONS: The predictive value of PET perfusion-based functional parameters outperforms the standard CT-based dose-volume parameters for the risk of grade ≥2 acute RILT. Functional parameters could be useful for guiding radiation therapy planning and reducing the risk of acute RILT.

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

How to contour the different heart subregions for future deep-learning modeling of the heart: A practical pictorial proposal for radiation oncologists.

There are currently no accurate rules for manually delineating the subregions of the heart (cavities, vessels, aortic/mitral valves, Planning organ at Risk Volumes for coronary arteries) with the perspective of deep-learning based modeling. Our objective was to present a practical pictorial view for radiation oncologists, based on the RTOG atlas and anatomical complementary considerations for the cases where the RTOG guidelines are missing.

Lung Stereotactic Body Radiation Therapy in a Patient with Severe Lung Function Impairment Allowed by Gallium-68 Perfusion PET/CT Imaging: A Case Report.

Lung stereotactic body radiotherapy (SBRT) is increasingly proposed, especially for patients with poor lung function who are not eligible for surgery. However, radiation-induced lung injury remains a significant treatment-related adverse event in these patients. Moreover, for patients with very severe COPD, we have very few data about the safety of SBRT for lung cancer. We present the case of a female with very severe chronic obstructive pulmonary disease (COPD) with a forced expiratory volume in one second (FEV1) of 0.23 L (11%), for whom a localized lung tumor was found. Lung SBRT was the only possible treatment. It was allowed and safely performed, based on a pre-therapeutic evaluation of regional lung function with Gallium-68 perfusion lung positron emission tomography combined with computed tomography (PET/CT). This is the first case report to highlight the potential use of a Gallium-68 perfusion PET/CT in order to safely select patients with very severe COPD who can benefit from SBRT.

The abscopal effect of immune-radiation therapy in recurrent and metastatic cervical cancer: a narrative review.

Despite human papillomavirus vaccination and screening, in about 5% of cases, cervical cancer (CC) is discovered at an initial metastatic stage. Moreover, nearly one-third of patients with locally advanced CC (LACC) will have a recurrence of their disease during follow-up. At the stage of recurrent or metastatic CC, there are very few treatment options. They are considered incurable with a very poor prognosis. For many years, the standard of care was the combination of platinum-based drug and paclitaxel with the possible addition of bevacizumab. The most recent years have seen the development of the use of immune checkpoint inhibitors (ICIs) (pembrolizumab, cemiplimab and others) in patients with CC. They have shown long term responses with improved overall survival of patients in 1st line (in addition to chemotherapy) or 2nd line (as monotherapy) treatment. Another emerging drug is tisotumab vedotin, an antibody-drug conjugate targeting tissue factor. Radiation therapy (RT) often has a limited palliative indication in metastatic cancers. However, it has been observed that RT can induce tumor shrinkage both in distant metastatic tumors beyond the radiation field and in primary irradiated tumors. This is a rarely observed phenomenon, called abscopal effect, which is thought to be related to the immune system and allows a tumor response throughout the body. It would be the activation of the immune system induced by the irradiation of cancer cells that would lead to a specific type of apoptosis, the immunogenic cell death. Today, there is a growing consensus that combining RT with ICIs may boost abscopal response or cure rates for various cancers. Here we will review the potential abscopal effect of immune-radiation therapy in metastatic cervical cancer.